A number of systems and programs are offered on the market for the design, the engineering and the manufacturing of objects. CAD is an acronym for Computer-Aided Design, e.g. it relates to software solutions for designing an object. CAE is an acronym for Computer-Aided Engineering, e.g. it relates to software solutions for simulating the physical behavior of a future product. CAM is an acronym for Computer-Aided Manufacturing, e.g. it relates to software solutions for defining manufacturing processes and operations. In such computer-aided design systems, the graphical user interface plays an important role as regards the efficiency of the technique. These techniques may be embedded within Product Lifecycle Management (PLM) systems. PLM refers to a business strategy that helps companies to share product data, apply common processes, and leverage corporate knowledge for the development of products from conception to the end of their life, across the concept of extended enterprise. The PLM solutions provided by Dassault Systèmes (under the trademarks CATIA, ENOVIA and DELMIA) provide an Engineering Hub, which organizes product engineering knowledge, a Manufacturing Hub, which manages manufacturing engineering knowledge, and an Enterprise Hub which enables enterprise integrations and connections into both the Engineering and Manufacturing Hubs. All together the system delivers an open object model linking products, processes, resources to enable dynamic, knowledge-based product creation and decision support that drives optimized product definition, manufacturing preparation, production and service.
These systems may fall within or overlap the fields of computer vision and/or virtual reality. In these fields, different solutions exist that provide localizing a 3D modeled object in a 3D scene, the 3D modeled object and the 3D scene each including respective 3D points, each 3D point being associated to a respective normal vector. Localizing such a 3D modeled object in such a 3D scene may indeed prove useful for many applications, such as 3D reconstruction (e.g. structure-from-motion analysis or multi-view reconstruction) and virtual reality (e.g. markerless augmented reality). In these applications, localizing a specific 3D modeled object in a 3D scene may improve the experience in several known ways.
In this context, some solutions disclose a method comprising a positioning of the 3D modeled object in the 3D scene, the positioning being performed following an algorithm that rewards, for each of first couples made of two 3D points of the 3D modeled object and their respective associated normal vectors, a match with a respective second couple made of two 3D point of the 3D scene and its respective associated normal vectors, the first couple being positioned substantially on the second couple, the match between the first couple and the second couple amounting to a substantial equality between the value of a descriptor for the first couple and the value of the descriptor for the second couple. Distinct examples of such a type of solution include paper “Bertram Drost, Slobodan Ilic, 3D Object Detection and Localization using Multimodal Point Pair Features, Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2012”, paper “Bertram Drost, Markus Ulrich, Nassir Navab, Slobodan Ilic, Model Globally, Match Locally: Efficient and Robust 3D Object Recognition, Conference on Computer vision and pattern recognition (CVPR), 2010” and European patent EP2385483.
However, there is still a need to improve on the accuracy or relevance of the result and/or on computation time, when localizing a 3D modeled object in a 3D scene.